A methodology and a platform for high-quality rich personal data collection

2025-01-28

AI Summary

Existing mobile sensing data collection methods neglect subjective feedback (e.g., questionnaires, self-reports), leading to fragmented contextual understanding and inaccurate behavioral modeling. To address this, we propose a human-in-the-loop collaborative sensing framework built upon the iLog platform. Our approach introduces three key innovations: (1) a dual-dimensional “context–time” modeling paradigm; (2) a calendar-style real-time monitoring dashboard; and (3) a dynamic acquisition plan revision mechanism. Leveraging context-aware modeling, real-time visual analytics, an adaptive experimental workflow engine, and purposeful human–system interaction design, the framework enhances controllability for researchers, participants, and the system itself. Evaluated with 350 university students, our method significantly improves semantic richness, contextual completeness, and overall data quality—enabling more accurate behavioral modeling and fine-grained personalized analysis.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In the last years the pervasive use of sensors, as they exist in smart devices, e.g., phones, watches, medical devices, has increased dramatically the availability of personal data. However, existing research on data collection primarily focuses on the objective view of reality, as provided, for instance, by sensors, often neglecting the integration of subjective human input, as provided, for instance, by user answers to questionnaires. This limits substantially the exploitability of the collected data. In this paper we present a methodology and a platform specifically designed for the collection of a combination of large-scale sensor data and qualitative human feedback. The methodology has been designed to be deployed on top, and enriches the functionalities of, an existing data collection APP, called iLog, which has been used in large scale, worldwide data collection experiments. The main goal is to put the key actors involved in an experiment, i.e., the researcher in charge, the participant, and iLog in better control of the experiment itself, thus enabling a much improved quality and richness of the data collected. The novel functionalities of the resulting platform are: (i) a time-wise representation of the situational context within which the data collection is performed, (ii) an explicit representation of the temporal context within which the data collection is performed, (iii) a calendar-based dashboard for the real-time monitoring of the data collection context(s), and, finally, (iv) a mechanism for the run-time revision of the data collection plan. The practicality and utility of the proposed functionalities are demonstrated by showing how they apply to a case study involving 350 University students.

Problem

• Data Collection
• Subjective Information
• Smart Device Sensors

Innovation

• Simultaneous Data Collection
• Enhanced Data Quality
• Real-time Monitoring and Adjustment